
PPI [1]} consists of 24 graphs corresponding to different human tissues, where 20 graphs are used for training, two graphs are used for validation, and another 2 graphs are used for testing. The average node numbers for each graph is 2372. Each node has 50 features, consisting of positional gene sets, motif gene sets, and immunological signatures. The number of labels is 121 and a node can have multiple labels at the same time. PPI dataset is used for inductive learning.

Coauthor-CS and Coauthor-Physics [2]} are co-author graphs which belong to the field of computer science and physics, respectively. Authors are denoted as nodes, and edges indicate whether there is a cooperative relationship between two authors. Node features represent the keywords of each author's paper, and labels indicate each author's most active research field. Coauthor-CS/Physics datasets are used for transductive learning.

Amazon-Computer and Amazon-Photo [2]}, [4]} are parts of Amazon co-purchase graph, where the nodes represent products, and the edges represent whether two products are always purchased together. Node features represent product reviews encoded in bag-of-words, and labels are given by product categories. These two datasets are used for transductive learning.

